Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

Anam, M. Khairul; Mahendra, Muhammad Ihza; Agustin, Wirta; Rahmaddeni, Rahmaddeni; Nurjayadi, Nurjayadi

doi:10.29407/intensif.v6i1.15870

Cited by 12 publications

(9 citation statements)

References 49 publications

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“…According to [32][33], XGBoost is an algorithm that is improved based on gradient boosting decision trees and can form boosted trees efficiently and work parallel. XGBoost is also one of the machine learning techniques to overcome regression and classification problems based on gradient boosting decision tree (GBDT) [27].…”

Section: Xgboost (Extreme Gradient Boosting)mentioning

confidence: 99%

The Application of Naïve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media

Putra¹,

Agustin²,

Anam³

et al. 2022

Self Cite

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<p>There are problems that still exist in online learning including limited-reach networks, inadequate facilities and infrastructure, and others. This study discussed the analysis of sentiment which used the Naïve Bayes Classifier (NBC) method with XGBoost feature selection as a performance improvement that took data from news portals. The results of this study showed that graph data on the application of online learning forms in Indonesia had a "Negative" opinion. Performance testing of the NBC method based on XGBoost feature selection was conducted four times. The first experiment resulted in an accuracy value of 60.18% with 50/50 split data. The next experiment had an accuracy value of 56.92% with 70/30 split data. After that, the third experiment resulted in an accuracy value of 65.90% with 80/20 split data. The result of the last experiment was an accuracy value of 63.63% with 90/10 split data. After using XGBoost feature selection, it produced an accuracy of 60.18%, 67.69%, 70.45%, and 77.27%. The study also produced the highest average score at 10-Fold Cross-Validation in the second trial with a score of 65.62%.</p>

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Section: Xgboost (Extreme Gradient Boosting)mentioning

confidence: 99%

The Application of Naïve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media

Putra¹,

Agustin²,

Anam³

et al. 2022

Self Cite

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“…1) Cleansing, At this stage, comments are cleaned from words that are not needed to reduce noise. Emissions in comments are HTML characters, emoticons, hashtags (#), username (@username), and URLs [20].…”

Section: E Pre-processingmentioning

confidence: 99%

Sentiment Analysis of the Convict Assimilation Program on Handling Covid-19

Ulfah¹,

Anam²,

Munti

et al. 2022

JUITA

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Coronavirus Disease-19 (Covid-19) is an infectious disease caused by the SARS-CoV-2 virus. The rapid spread of this disease has affected 216 other countries and regions, including Indonesia. In minimizing the spread and increasing losses, it is necessary to have several policies made by the Indonesian government in dealing with this. One of the policies taken by the government is the Convict Assimilation Program to prevent the spread of the virus in prisons. The Prisoner Assimilation Program fosters inmates by integrating prisoners into social life. Many media reported on the assimilation program in various media, including news portals, so that it became a forum for the public to express their opinions. News portals can be a source for getting public opinion. Therefore, sentiment analysis can be done to determine the sentiment of any existing public opinion. In this study, the analysis was carried out by applying one of the data mining methods, namely the Support Vector Machine, with positive, negative, and neutral sentiment labeling. The data used is audience comments in Indonesian with a dataset of 404 comments and then resampled so that the number of data becomes 669. The analysis uses the kernel Radial Basis Function (RBF), RBF with Grid Search, Polynomials, and Polynomials with grid search. Kernel RBF and Kernel Polynomial with Grid Search comparing test and training data 80%:20% with the highest accuracy of 95%.

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“…Dan yang terakhir adalah Iriadi & Nuraeni (2016) memprediksi kelayakan kredit pada bank mayapada jakarta. Dalam melakukan klasifikasi data mining ada beberapa metode yang dapat digunakan seperti algoritma C4.5 (Ardiansyah et al, 2021), Support Vector Machines (SVM) (Anam et al, 2022), Algoritma K-Nearest Neighbor (KNN) (Hidayat et al, 2021;Prasetio et.al, 2020), dan lain sebagainya.…”

Section: Pendahuluanunclassified

Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor

Efriadi¹,

Rahmaddeni²,

Agustin³

et al. 2022

Edumatic

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There are several issues with Social Security Organizing Agency (BPJS) employment at the moment, one of which is contribution receivable. To reduce the BPJS contribution receivables, BPJS has done various ways. However, the resulting effort is not maximal enough to reduce the number of receivables in BPJS. This study aims to provide input by predicting the addition of receivables from social security contributions made by several companies or organizations. This study used the K-Nearest Neighbor (KNN) Algorithm with a cross-validation technique. KNN is a very simple classification method in classifying an image based on the closest distance to its neighbors. This study conducted data processing from BPJS use, which amounted to 1193 data. The data is then preprocessed so that the processed data is clean from missing and noise, this data uses 70:30 data splitting. After the preprocessing and splitting of data were carried out, the next step was to do modeling using KNN, so the cross-validation to improve the accuracy of results obtained from the KNN algorithm. The results obtained from this research get the highest accuracy of 92% with the Optimal K value being 6, then the ROC curve gets 94% accuracy. From these results, it can be said that the use of cross-validation can increase the accuracy of this study.

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Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

Cited by 12 publications

References 49 publications

The Application of Naïve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media

The Application of Naïve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media

Sentiment Analysis of the Convict Assimilation Program on Handling Covid-19

Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor

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